Enter the exciting world of a data analyst

Wanting to indulge in the passion of working with information, discovering the hidden insights in detail, and presenting these insights in a simplistic way can be very fascinating for some people. This article will lead you through the knowledge, resources, and tools needed to become a data analyst.

It is possible to equate data analytics to how you make a jigsaw puzzle. Your first step is to collect all the pieces of the puzzle and then correctly match them to bring out the final image. In a similar way, you have to analyze data gathered from multiple sources in data analytics, clean it, and then turn it into information that can be interpreted by general people.

What is data analytics?

The word "analyze" can mean evaluating something from the perspective of a wider angle to extract useful knowledge from it. Data analytics can, therefore, be characterized as the process in which, by scrutinizing it, concrete insights are derived from raw data. Your company knowledge, product developments, industry dynamics, etc. could be such insights.

In nature, the data collected may be structured, semi-structured, or unstructured. It is possible to visually depict the end outcome as graphs and charts that provide detailed results of the analysis. In the research process, several instruments and structures are used.

For professionals who can help organizations translate raw data into usable knowledge, there is a high requirement, which in turn can help the company's growth. In the field of data analytics, there are many job positions, and within the job pool, being a data analyst provides the most impressive career opportunities.

What does it take to become a data analyst?

Given below are some skills that are needed to become a top-notch data analyst:

Market/Domain Knowledge: 

Domain knowledge means knowing the business conditions of clients, rivals, and the overall near future of the organization. Every data analyst must spend enough time on acquiring the knowledge of the company/domain relevant to the problem statement. This will provide you with the skill to understand the dilemma from multiple viewpoints and come up with the most effective approach.

The right tools:

It doesn't take a technical background to master these data resources. On a click and drag basis, most of these methods work. The critical element is to understand the different functionalities needed to use these tools to analyze and visualize data. Excel is another commonly used technique for data processing, but it has been overlooked for its capability. In Excel, there are many features such as pivot tables, data manipulation formulas, and visualization charts that are used effectively to build some impressive dashboards.

Programming:

Using Python and its vast libraries for visual analytics is the omnipresent choice. For those new to coding, Python is the best choice. It is really easy to comprehend and the most commonly used programming language in data science. It is also useful to have basic knowledge of SQL as it will give you leverage over accessing data from multiple sources. In order to handle the data well, understanding data extraction and integration can help.

Jobs that need an understanding of data analytics

Business Intelligence Analyst

The most fundamental task of a business intelligence analyst is to identify trends in their company and market data and value. This is sort of a position of a data analyst at most businesses. BI Analysts are supposed to be experienced in analyzing information, working with SQL, and visualizing and modeling data.

Data analyst

In order to find value and opportunities, Data Analysts do just what the job description implies: evaluate market and industry data. In any sector, data analysts can be found, and job titles can differ. "Healthcare data analyst." "Business analyst" and similarly-named positions also share a lot with data analyst roles. 

Data engineer

Data engineers also concentrate on broader databases and are charged with improving the architecture around various processes of data analytics. 

A data engineer, for instance, might concentrate on the data capture process to make an acquisition pipeline more effective.

In the end, what matters is the passion that you have for learning a skill. Data analytics has a great amount of competition and also a high amount of paychecks. 

Some of the given below articles will interest you:

• The scope of an MBA in Data Analytics and Big Data
• 5 Excel interview questions that will help you prepare for interviews or assessments
• How an internship at Tropical Animal Genetics sharpened my skills in data analytics | Mrityunjay’s story
• How an internship at Wipro made me think out of the box in the domain of Data Analytics and AI | Tanya’s story
• IIT Kharagpur invites students to learn Data Analytics